模式识别与人工智能
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  2010, Vol. 23 Issue (6): 856-861    DOI:
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K-L Divergence Based Model Clustering Method for Fast Speaker Identification
WANG Huan-Liang1,2,HAN Ji-Qing1,ZHENG Gui-Bin1
1.School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001
2.College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266035

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Abstract  With the increase of enrolled speakers and audio data to be recognized, the conventional speaker identification methods can not meet the real-time demand for internet application environment. A K-L divergence based speaker model clustering method is proposed to construct a hierarchical identification system, which remarkably improves the recognition efficiency. Moreover, the confidence measure using class-level identification information is also investigated to effectively exclude out-of-set speaker as early as possible. The experimental results show the proposed method averagely increases the identification speed by 3.2 times while the error rate of closed-set identification only increases about 0.9% compared with the conventional method. The open-set identification can be speeded up by using class-level confidence measure and a relatively 5.1% error rate reduction can be achieved on out-of-set speakers identification while keeping the identification performance of in-set speakers unchanged.
Key wordsK-L Divergence      Model Clustering      Confidence Measure      Speaker Identification      Internet Environment     
Received: 09 February 2009     
ZTFLH: TN912.3  
  TP391.4  
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